def create_fake_quantize_node(graph: Graph, name):
fq = FakeQuantize(graph, {
'name': name,
'levels': 0,
'stop_value_propagation': True
}).create_node()
input_low = Const(graph, {
'value': np.array(0.0).astype(np.float32)
}).create_node()
input_height = Const(graph, {
'value': np.array(0.0).astype(np.float32)
}).create_node()
output_low = Const(graph, {
'value': np.array(0.0).astype(np.float32)
}).create_node()
output_height = Const(graph, {
'value': np.array(0.0).astype(np.float32)
}).create_node()
input_low.out_port(0).connect(fq.in_port(1))
input_height.out_port(0).connect(fq.in_port(2))
output_low.out_port(0).connect(fq.in_port(3))
output_height.out_port(0).connect(fq.in_port(4))
input_low.infer(input_low)
input_height.infer(input_height)
output_low.infer(output_low)
output_height.infer(output_height)
return fq
def replace_sub_graph(self, graph: Graph, match: [dict, SubgraphMatch]):
q = match['quantize']
dq = match['dequantize']
q_scale = q.in_port(1).get_source().node
q_zerop = q.in_port(2).get_source().node
dq_scale = dq.in_port(1).get_source().node
dq_zerop = dq.in_port(2).get_source().node
inp_port = q.in_port(0).get_source()
name = inp_port.node.soft_get('name', inp_port.node.id)
# only constant as for zero_point/scale supported
if q_scale.soft_get('type') == 'Const' and dq_scale.soft_get('type') == 'Const' and \
q_zerop.soft_get('type') == 'Const' and dq_zerop.soft_get('type') == 'Const':
# only patterns with same scale/zero_point values for Q and DQ are supported
if q_scale.value == dq_scale.value and q_zerop.value == dq_zerop.value:
log.debug('Found Q-DQ pattern after {}'.format(name))
zero_point_type = q_zerop.value.dtype
# data type affects range of output values: [-128..127] or [0..255]
if zero_point_type == np.int8:
output_min_value = -128.0
output_max_value = 127.0
elif zero_point_type == np.uint8:
output_min_value = 0.0
output_max_value = 255.0
else:
raise Error('Not supported type {} for zero point value in node {}'.format(
zero_point_type, q_zerop.soft_get('name')))
min_value = q_scale.value * (output_min_value - q_zerop.value)
max_value = q_scale.value * (output_max_value - q_zerop.value)
input_min = Const(graph, {'value': np.array(min_value)}).create_node()
input_max = Const(graph, {'value': np.array(max_value)}).create_node()
FQ = FakeQuantize(graph, {
'levels': 256,
'name': match['quantize'].name + '_Dequantize/FakeQuantize'
}).create_node()
FQ.in_port(0).connect(match['quantize'].in_port(0).get_source())
FQ.in_port(1).connect(input_min.out_port(0))
FQ.in_port(2).connect(input_max.out_port(0))
FQ.in_port(3).connect(input_min.out_port(0))
FQ.in_port(4).connect(input_max.out_port(0))
match['dequantize'].out_port(0).get_connection().set_source(FQ.out_port(0))
dq_name = match['dequantize'].soft_get('name', match['dequantize'].id)
rename_nodes([(match['dequantize'], dq_name + '/to_be_removed'), (FQ, dq_name)])
else:
raise Error('QuantizeLinear and DequantizeLinear (after {}) have different scale or zero-point values, '
'cannot fuse into FakeQuantize!'.format(name))